Literature DB >> 21284013

Effective reproduction numbers are commonly overestimated early in a disease outbreak.

G N Mercer1, K Glass, N G Becker.   

Abstract

Reproduction numbers estimated from disease incidence data can give public health authorities valuable information about the progression and likely size of a disease outbreak. Here, we show that methods for estimating effective reproduction numbers commonly give overestimates early in an outbreak. This is due to many factors including the nature of outbreaks that are used for estimation, incorrectly accounting for imported cases and outbreaks arising in subpopulations with higher transmission rates. Awareness of this bias is necessary to correctly interpret estimates from early disease outbreak data.
Copyright © 2011 John Wiley & Sons, Ltd.

Mesh:

Year:  2011        PMID: 21284013     DOI: 10.1002/sim.4174

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  23 in total

1.  Estimating reproduction numbers for adults and children from case data.

Authors:  K Glass; G N Mercer; H Nishiura; E S McBryde; N G Becker
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4.  Impact of public health interventions to curb SARS-CoV-2 spread assessed by an evidence-educated Delphi panel and tailored SEIR model.

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Journal:  Z Gesundh Wiss       Date:  2021-05-17

5.  Can antiviral drugs contain pandemic influenza transmission?

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Journal:  PLoS One       Date:  2011-03-28       Impact factor: 3.240

6.  Estimating the transmission potential of supercritical processes based on the final size distribution of minor outbreaks.

Authors:  Hiroshi Nishiura; Ping Yan; Candace K Sleeman; Charles J Mode
Journal:  J Theor Biol       Date:  2011-11-07       Impact factor: 2.691

7.  Connecting network properties of rapidly disseminating epizoonotics.

Authors:  Ariel L Rivas; Folorunso O Fasina; Almira L Hoogesteyn; Steven N Konah; José L Febles; Douglas J Perkins; James M Hyman; Jeanne M Fair; James B Hittner; Steven D Smith
Journal:  PLoS One       Date:  2012-06-25       Impact factor: 3.240

8.  Using Combined Diagnostic Test Results to Hindcast Trends of Infection from Cross-Sectional Data.

Authors:  Gustaf Rydevik; Giles T Innocent; Glenn Marion; Ross S Davidson; Piran C L White; Charalambos Billinis; Paul Barrow; Peter P C Mertens; Dolores Gavier-Widén; Michael R Hutchings
Journal:  PLoS Comput Biol       Date:  2016-07-06       Impact factor: 4.475

9.  Seasonal Influenza Forecasting in Real Time Using the Incidence Decay With Exponential Adjustment Model.

Authors:  Tahmina Nasserie; Ashleigh R Tuite; Lindsay Whitmore; Todd Hatchette; Steven J Drews; Adriana Peci; Jeffrey C Kwong; Dara Friedman; Gary Garber; Jonathan Gubbay; David N Fisman
Journal:  Open Forum Infect Dis       Date:  2017-09-27       Impact factor: 3.835

10.  Historical data and modern methods reveal insights in measles epidemiology: a retrospective closed cohort study.

Authors:  Beverley J Paterson; Martyn D Kirk; A Scott Cameron; Catherine D'Este; David N Durrheim
Journal:  BMJ Open       Date:  2013-01-11       Impact factor: 2.692

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